Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Explainable Deepfake Video Detection using Convolutional Neural Network and CapsuleNet (2404.12841v1)

Published 19 Apr 2024 in cs.CV, cs.LG, and eess.IV

Abstract: Deepfake technology, derived from deep learning, seamlessly inserts individuals into digital media, irrespective of their actual participation. Its foundation lies in machine learning and AI. Initially, deepfakes served research, industry, and entertainment. While the concept has existed for decades, recent advancements render deepfakes nearly indistinguishable from reality. Accessibility has soared, empowering even novices to create convincing deepfakes. However, this accessibility raises security concerns.The primary deepfake creation algorithm, GAN (Generative Adversarial Network), employs machine learning to craft realistic images or videos. Our objective is to utilize CNN (Convolutional Neural Network) and CapsuleNet with LSTM to differentiate between deepfake-generated frames and originals. Furthermore, we aim to elucidate our model's decision-making process through Explainable AI, fostering transparent human-AI relationships and offering practical examples for real-life scenarios.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (49)
  1. “Mesonet: a compact facial video forgery detection network” In 2018 IEEE International Workshop on Information Forensics and Security (WIFS), 2018, pp. 1–7 IEEE
  2. “Detecting deep-fake videos from phoneme-viseme mismatches” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 660–661
  3. “Recycle-gan: Unsupervised video retargeting” In Proceedings of the European conference on computer vision (ECCV), 2018, pp. 119–135
  4. Christopher M Bishop “Pattern recognition and machine learning” springer, 2006
  5. BuzzFeedVideo “You Won’t Believe What Obama Says In This Video!”, Available: https://www.youtube.com/watch?v=cQ54GDm1eL0, 2018
  6. “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8789–8797
  7. François Chollet “Xception: Deep learning with depthwise separable convolutions” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251–1258
  8. Umur Aybars Ciftci, Ilke Demir and Lijun Yin “Fakecatcher: Detection of synthetic portrait videos using biological signals” In IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE, 2020
  9. Edmar RS De Rezende, Guilherme CS Ruppert and Tiago Carvalho “Detecting computer generated images with deep convolutional neural networks” In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2017, pp. 71–78 IEEE
  10. “Deep generative image models using a laplacian pyramid of adversarial networks” In arXiv preprint arXiv:1506.05751, 2015
  11. “The deepfake detection challenge dataset” In arXiv preprint arXiv:2006.07397, 2020
  12. “Text-based editing of talking-head video” In ACM Transactions on Graphics (TOG) 38.4 ACM New York, NY, USA, 2019, pp. 1–14
  13. Gabriela Galindo “XR Belgium posts deepfake of Belgian premier linking Covid-19 with climate crisis” In The Brussels Times, 2020 URL: https://www.brusselstimes.com/news/belgium-all-news/politics/106320/xr-belgium-posts-deepfake-of-belgian-premier-linking-covid-19-with-climate-crisis/
  14. “Generative adversarial nets. In NIPS”, 2014
  15. Luca Guarnera, Oliver Giudice and Sebastiano Battiato “Fighting Deepfake by Exposing the Convolutional Traces on Images” In IEEE Access 8 IEEE, 2020, pp. 165085–165098
  16. David Güera and Edward J Delp “Deepfake video detection using recurrent neural networks” In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2018, pp. 1–6 IEEE
  17. “Deep residual learning for image recognition” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778
  18. Geoffrey E Hinton, Alex Krizhevsky and Sida D Wang “Transforming auto-encoders” In International conference on artificial neural networks, 2011, pp. 44–51 Springer
  19. Olivia Holmes, Martin S Banks and Hany Farid “Assessing and improving the identification of computer-generated portraits” In ACM Transactions on Applied Perception (TAP) 13.2 ACM New York, NY, USA, 2016, pp. 1–12
  20. Chih-Chung Hsu, Yi-Xiu Zhuang and Chia-Yen Lee “Deep fake image detection based on pairwise learning” In Applied Sciences 10.1 Multidisciplinary Digital Publishing Institute, 2020, pp. 370
  21. Bogdan Iancu “Evaluating Google Speech-to-Text API’s Performance for Romanian e-Learning Resources.” In Informatica Economica 23.1, 2019
  22. Tackhyun Jung, Sangwon Kim and Keecheon Kim “DeepVision: Deepfakes Detection Using Human Eye Blinking Pattern” In IEEE Access 8 IEEE, 2020, pp. 83144–83154
  23. “Hiding traces of resampling in digital images” In IEEE Transactions on Information Forensics and Security 3.4 IEEE, 2008, pp. 582–592
  24. “Fast face-swap using convolutional neural networks” In Proceedings of the IEEE international conference on computer vision, 2017, pp. 3677–3685
  25. Yuezun Li, Ming-Ching Chang and Siwei Lyu “In ictu oculi: Exposing ai created fake videos by detecting eye blinking” In 2018 IEEE International Workshop on Information Forensics and Security (WIFS), 2018, pp. 1–7 IEEE
  26. “Exposing deepfake videos by detecting face warping artifacts” In arXiv preprint arXiv:1811.00656, 2018
  27. Ming-Yu Liu, Thomas Breuel and Jan Kautz “Unsupervised image-to-image translation networks” In arXiv preprint arXiv:1703.00848, 2017
  28. “The voice conversion challenge 2018: Promoting development of parallel and nonparallel methods” In arXiv preprint arXiv:1804.04262, 2018
  29. “Detection of gan-generated fake images over social networks” In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2018, pp. 384–389 IEEE
  30. Akul Mehra “Deepfake detection using capsule networks with long short-term memory networks”, 2020
  31. Todd K Moon “The expectation-maximization algorithm” In IEEE Signal processing magazine 13.6 IEEE, 1996, pp. 47–60
  32. Huy H Nguyen, Junichi Yamagishi and Isao Echizen “Capsule-forensics: Using capsule networks to detect forged images and videos” In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 2307–2311 IEEE
  33. “On resampling detection and its application to detect image tampering” In 2006 IEEE International Conference on Multimedia and Expo, 2006, pp. 1325–1328 IEEE
  34. Alec Radford, Luke Metz and Soumith Chintala “Unsupervised representation learning with deep convolutional generative adversarial networks” In arXiv preprint arXiv:1511.06434, 2015
  35. Rajeev Ranjan, Vishal M Patel and Rama Chellappa “Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition” In IEEE transactions on pattern analysis and machine intelligence 41.1 IEEE, 2017, pp. 121–135
  36. “Faceforensics++: Learning to detect manipulated facial images” In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 1–11
  37. “Recurrent convolutional strategies for face manipulation detection in videos” In Interfaces (GUI) 3.1, 2019
  38. Sara Sabour, Nicholas Frosst and Geoffrey E Hinton “Dynamic routing between capsules” In arXiv preprint arXiv:1710.09829, 2017
  39. “Learning from simulated and unsupervised images through adversarial training” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2107–2116
  40. “Very deep convolutional networks for large-scale image recognition” In arXiv preprint arXiv:1409.1556, 2014
  41. “Eye blink detection using facial landmarks” In 21st computer vision winter workshop, Rimske Toplice, Slovenia, 2016
  42. Supasorn Suwajanakorn, Steven M Seitz and Ira Kemelmacher-Shlizerman “Synthesizing obama: learning lip sync from audio” In ACM Transactions on Graphics (ToG) 36.4 ACM New York, NY, USA, 2017, pp. 1–13
  43. “Inception-v4, inception-resnet and the impact of residual connections on learning” In Proceedings of the AAAI Conference on Artificial Intelligence 31.1, 2017
  44. “Face2face: Real-time face capture and reenactment of rgb videos” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2387–2395
  45. Eric Tokuda, Helio Pedrini and Anderson Rocha “Computer generated images vs. digital photographs: A synergetic feature and classifier combination approach” In Journal of Visual Communication and Image Representation 24.8 Elsevier, 2013, pp. 1276–1292
  46. “FakeSpotter: A simple yet robust baseline for spotting AI-synthesized fake faces” In arXiv preprint arXiv:1909.06122, 2019
  47. Xin Yang, Yuezun Li and Siwei Lyu “Exposing deep fakes using inconsistent head poses” In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 8261–8265 IEEE
  48. Xu Zhang, Svebor Karaman and Shih-Fu Chang “Detecting and simulating artifacts in gan fake images” In 2019 IEEE International Workshop on Information Forensics and Security (WIFS), 2019, pp. 1–6 IEEE
  49. “Unpaired image-to-image translation using cycle-consistent adversarial networks” In Proceedings of the IEEE international conference on computer vision, 2017, pp. 2223–2232
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now